Automatic control of in-vehicle media

ABSTRACT

A method for automatically adjusting vehicle media may include obtaining personal data of a vehicle operator and obtaining contextual data about the vehicle operating. The method may further include generating a distraction-level value that represents a degree to which the vehicle operator may be distracted from the media. The method may further include generating a distraction threshold value. The method may further include determining that the distraction-level value exceeds the distraction threshold value and adjusting the media in response.

BACKGROUND

The present disclosure relates to in-vehicle media, and morespecifically, to automatic control of in-vehicle media.

Vehicle operators may listen to media while operating a vehicle. Toenjoy the full content of the media, vehicle operators may need toadjust the media while operating the vehicle.

SUMMARY

Some embodiments of the present disclosure can be illustrated as amethod for adjusting vehicle media. The method may include obtainingpersonal data of a vehicle operator. The vehicle operator may beoperating a vehicle while media playback occurs within the vehicle. Themethod may further include obtaining contextual data about theoperating. The method may further include generating a distraction-levelvalue based at least in part on the personal data. The distraction-levelvalue may represent a potential degree to which the vehicle operator isdistracted from the media. The method may further include generating adistraction threshold value based at least in part on the contextualdata. The method may further include comparing the distraction-levelvalue to the distraction threshold value. The method may further includedetermining at a first time that the distraction-level value exceeds thedistraction threshold value. The method may further include adjustingthe media in response to the determining at the first time.

Some embodiments of the present disclosure can be illustrated as asystem comprising a processor and a memory in communication with theprocessor, the memory containing program instructions that, whenexecuted by the processor, are configured to cause the processor toperform the aforementioned method.

Some embodiments of the present disclosure can be illustrated as acomputer program product, the computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a computer to causethe computer to perform the aforementioned method.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts an example system, in accordance with embodiments of thepresent disclosure.

FIG. 2 depicts a flowchart of an example method for adjusting media, inaccordance with embodiments of the present disclosure.

FIG. 3 depicts an example plot of generated distraction-level values anddistraction threshold values over time, in accordance with embodimentsof the present disclosure.

FIG. 4 depicts a flowchart of an example method for determining adistraction-level value, in accordance with embodiments of the presentdisclosure.

FIG. 5 depicts a flowchart of an example method for determining adistraction threshold value, in accordance with embodiments of thepresent disclosure.

FIG. 6 depicts an example system block diagram, in accordance withembodiments of the present disclosure.

FIG. 7 depicts an example method for training a neural network, inaccordance with embodiments of the present disclosure.

FIG. 8 depicts the representative major components of a computer systemthat may be used in accordance with embodiments of the presentdisclosure.

FIG. 9 depicts a cloud computing environment according to an embodimentof the present disclosure.

FIG. 10 depicts abstraction model layers according to an embodiment ofthe present disclosure.

FIG. 11 depicts an example neural network that may be specialized topredict a distraction-level value, in accordance with embodiments of thepresent disclosure.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to controlling in-vehiclemedia, more particular aspects relate to automatically controllingin-vehicle media. While the present disclosure is not necessarilylimited to such applications, various aspects of the disclosure may beappreciated through a discussion of various examples using this context.

Vehicle operators may listen to media, such as audiobooks and musicplaylists, while operating vehicles. Safe operation of a vehicle mayrequire a vehicle operator to divert attention from, or be distractedfrom, the media in order to manage the vehicle. For example, if avehicle suddenly encounters a severe storm or a foreign object on aroadway, the vehicle operator may need to adapt the vehicle (e.g. slowdown, change lanes, and/or activate windshield wipers) to maintainvehicle and/or passenger safety. However, such adaptations may cause thevehicle operator to be distracted from the in-vehicle media and miss atleast a portion of its content. Furthermore, it may be difficult orunsafe to adjust (e.g. pause, stop, mark a location of) the media whileadapting the vehicle.

To address these and other problems, embodiments of the presentdisclosure include a method for automatically adjusting in-vehicle mediabased on a determination that a vehicle operator is likely distractedfrom the media. The determination is, in some embodiments, based onobtained personal data of a vehicle operator and obtained contextualdata regarding the environment in which the vehicle operator operatesthe vehicle. For example, embodiments of the present disclosure mayautomatically adjust the in-vehicle media by pausing, marking, orstopping playback of the media. The automatic adjustment may be inresponse to analyzing data, such as the vehicle operator's heart ratevariance and a braking frequency of the vehicle, and determining thatthe vehicle operator is distracted from media, such as an audiobook,being played within the vehicle. As a result, embodiments of the presentdisclosure may allow the vehicle operator to enjoy the full content ofin-vehicle media despite circumstances that may temporarily require thevehicle operator to focus on stimuli other than the media.

Embodiments of the present disclosure may determine whether a vehicleoperator's potential level of distraction from in-vehicle media exceedsa threshold level of distraction, such that an automatic adjustment ofthe media may be warranted. The determination may be based on obtainedpersonal data of the vehicle operator as well as obtained contextualdata regarding the vehicle operator's operating environment.Furthermore, in some embodiments, the personal data may be analyzed andquantified as a distraction-level value. The distraction-level value mayrepresent a potential degree to which the vehicle operator is distractedfrom the media (e.g., a 0.10 distraction-level value may indicate thatthe vehicle operator may be highly distracted from the media, and a 0.90distraction-level value may indicate that the vehicle operator may bebarely distracted from the media). In some embodiments, the contextualdata may be analyzed and quantified as a distraction threshold value.The distraction threshold value may represent an estimated maximumamount of distraction a vehicle operator may sustain without beingdistracted from media playing within the vehicle, in view of thevehicle's environment and/or the circumstances surrounding the vehicle'soperation. In some embodiments, the distraction-level value may becompared to the distraction threshold value to determine whether thevehicle operator's potential level of distraction from the mediarequires that the media be adjusted (e.g. paused, stopped, marked forlater reference). In some embodiments, a processing device may betrained to determine a vehicle operator's potential level of distractionfrom media by applying artificial intelligence to the obtained personaldata and the obtained contextual data. Embodiments of the presentdisclosure may determine when to automatically adjust the media byincorporating or not incorporating analysis by one or more neuralnetworks.

The personal data of the vehicle operator that may be used to determinea distraction-level value may include metrics, such as heart rate, heartrate variance, and images of the vehicle operator. Such metrics may becorrelated with the vehicle operator's potential level of distractionfrom media while operating the vehicle.

For example, in some embodiments, the vehicle operator's heart rate mayindicate the vehicle operator's mental state; thus, it may imply thevehicle operator's level of distraction from the media. For example, afaster heart rate, such as 100 beats per minute (bpm) may indicate thatthe vehicle operator is stressed or anxious and more likely to bedistracted from media while operating the vehicle. In contrast, a lowerheart rate, such as 60 bpm may indicate that the vehicle operator iscalm and more likely to be attentive to media while operating thevehicle.

Furthermore, in some embodiments the vehicle operator's heart ratevariance (i.e. variations in the time interval between consecutiveheartbeats) may similarly indicate the vehicle operator's level ofdistraction from the media. For example, inconsistent heart ratevariances may indicate that a vehicle operator is frustrated and lesslikely to be attentive to media while operating a vehicle.

In some embodiments, images of the vehicle operator may indicate thevehicle operator's level of distraction from media while operating thevehicle. Such images may include photographs and/or video recordings,such as a live video feed to monitor the vehicle operator's behavior.For example, one or more cameras inside the vehicle may show that thevehicle operator is unlikely to be attentive to the media (e.g., thevehicle operator may be looking into the rear of the vehicle or in adirection other than the direction in which the vehicle is moving and/orexhibiting facial expressions indicative of stress, strong emotion, ordrowsiness). Such images may be analyzed via a processor using imagingtechniques and artificial intelligence to predict a vehicle operator'smental state and/or whether the vehicle operator is likely distractedfrom the media.

Next, contextual data regarding the vehicle operator's surroundingsduring operation of the vehicle may be used to determine a distractionthreshold value. Contextual data may include accelerometer data, vehicle“Internet of Things” (“IoT”) sensor data, weather data, location data,and/or video images of the interior or vicinity of the vehicle.

In some embodiments, accelerometer data may provide relevant informationabout the vehicle's operation. For example, accelerometer data mayprovide information, such as how sharply the vehicle is being turned,whether the vehicle is rapidly accelerating and/or decelerating, and/orwhether the vehicle is swerving. Accordingly, the accelerometer data mayassist in characterizing the vehicle operator's driving behavior and/ordetermining whether a present driving behavior diverges from a typicaldriving behavior.

In some embodiments, vehicle IoT sensors may also provide relevantinformation about the vehicle's operation. Vehicle IoT sensors mayinclude devices, such as cameras, braking sensors, global positioningsystems, speedometers, and accelerometers. For example, a braking sensormay provide information about the vehicle operator's braking frequency.Additionally, a vehicle camera may provide information about theproximity of the vehicle to other objects. A vehicle camera may alsoprovide information about the interior of the vehicle, such as whetherobjects in the vehicle may be blocking the vehicle operator'svisibility, or whether other passengers are traveling in the vehicle.

In some embodiments, vehicle weather sensors may provide informationabout the environment in which the vehicle is operated. For example,such sensors may detect parameters, such as precipitation, fog, or icyroad conditions.

In some embodiments, location sensors may provide additional informationabout the vehicle's environment. For example, a location sensor, such asa global positioning system device, may detect information, such aswhether a currently traversed route is frequently traveled or new,whether a vehicle is being operated in a construction zone, or whether avehicle is being operated in traffic.

By considering both the personal data and the contextual data,embodiments of the present disclosure may tailor a determination toadjust the media according to a mental state of the vehicle operator andan environment of the vehicle operator. As a result, embodiments of thepresent disclosure may accurately determine when a media adjustment maybe warranted.

It is to be understood that the aforementioned advantages are exampleadvantages and should not be construed as limiting. Embodiments of thepresent disclosure can contain all, some, or none of the aforementionedadvantages while remaining within the spirit and scope of the presentdisclosure.

Turning to the figures, FIG. 1 illustrates an example embodiment of asystem 100 according to the present disclosure. In the illustratedembodiment, the system 100 includes a vehicle 110 equipped with astorage/processing device 120. The storage/processing device 120 mayperform functions, such as obtaining, storing, analyzing, and processingdata from one or more weather sensors 170, one or more IoT devices 140,mobile device 130, and/or wearable device 160. The storage/processingdevice may also utilize artificial intelligence and machine learning toprocess and analyze data. Additionally, the storage/processing devicemay adjust media playing within the vehicle 110. In some embodiments,the storage/processing device 120 may execute computer-readableinstructions to perform one or more of the methods described herein.

For example, the storage/processing device 120 may communicate with thevehicle weather sensor(s) 170, vehicle IoT device(s) 140, mobile device130, and/or wearable device(s) 160 to obtain personal data of thevehicle operator 150 and contextual data regarding the vehicleoperator's environment. The storage/processing device may then generatea distraction-level value to predict a likelihood that the vehicleoperator is distracted from the media. The storage/processing device mayalso generate a distraction threshold value and compare it to thedistraction-level value. Upon determining that the distraction-levelvalue exceeds the distraction threshold value, the storage/processingdevice may adjust the media.

Vehicle 110 may be of any type used for transportation consistent withthe embodiments of this disclosure, such as an automobile, motorcycle,watercraft, or aircraft. Weather sensor(s) 170 may include devices formeasuring properties, such as temperature, humidity, fog, andprecipitation. IoT device(s) 140 may include devices, such as cameras,braking sensors, global positioning systems, speedometers, andaccelerometers.

Additionally, system 100 may include a mobile device 130, such as amobile phone, and/or a wearable device 160, such as a smartwatch orfitness monitor, worn by a vehicle operator 150. Mobile device 130 mayinclude additional devices, such as a camera, global positioning system,and accelerometer. Wearable device 160 may be capable of measuringbiometric properties, such as the vehicle operator's heart rate andheart rate variance over time.

FIG. 2 illustrates an embodiment of an example method 200 for adjustingthe media according to the present disclosure. One or more operations ofmethod 200 may be performed by a processor (e.g., storage/processingdevice 120 of FIG. 1). Method 200 includes a step 210 to obtain personaldata of the vehicle operator. The personal data may include information,such as heart rate measurements, heart rate variance measurements, andimages of the vehicle operator. The obtained personal data may be usedto generate a distraction-level value in step 220.

For example, the vehicle operator's heart rate measurements may indicatea mental state of the vehicle operator. Such a mental state may imply alikeliness that the vehicle operator is distracted from the media. Thislikeliness can be quantified as an implied distraction value andcombined with additional implied distraction values to generate adistraction-level value.

In an example, a vehicle operator's measured heart rate of 130 beats perminute may indicate that the vehicle operator is anxious. Furthermore,anxiety may be correlated with a 70% likelihood that the vehicleoperator will be distracted from the media, which may be quantified as a0.70 implied distraction value. Thus, the measured heart rate of 130beats per minute may be correlated with the 0.70 implied distractionvalue. Similarly, the vehicle operator's measured heart rate varianceover time may correspond to a profile of someone who is angry.Furthermore, anger may be associated with an 80% likelihood that thevehicle operator will be distracted from the media, which may bequantified as a 0.80 implied distraction value. Accordingly, the vehicleoperator's measured heart rate variance may be correlated with the 0.80implied distraction value. To generate a distraction-level value, thetwo implied distraction values may be averaged to obtain a 0.75distraction-level value.

Step 230 includes obtaining contextual data regarding the vehicleoperator's surroundings during operation of the vehicle. The contextualdata may contain information, such as accelerometer measurements,location data, and weather data. The obtained contextual data may beused to generate a distraction threshold value in step 240.

For example, a measured braking frequency of 15 events per minute mayindicate that the vehicle is in stop-and-go traffic. Furthermore, thestop-and-go traffic environment may be correlated with an impliedthreshold value of 0.90 because the vehicle operator may have a lowlikelihood of being distracted from the media during stop-and-gotraffic. Additionally, the detection of icy road conditions may becorrelated with an implied threshold value of 0.50 because the vehicleoperator may be more likely to be distracted from the media when drivingon an icy road. To generate a distraction threshold value, the twoimplied distraction values may be averaged to obtain a 0.70 distractionthreshold value.

Step 250 includes comparing the generated distraction-level value to thegenerated distraction threshold value to determine whether thedistraction-level value exceeds the distraction threshold value. Forexample, continuing with the examples above, in step 250, the 0.75distraction-level value would be determined to exceed the 0.70distraction threshold value.

Step 260 includes adjusting the media in response to determining thatthe distraction-level value exceeds the distraction threshold value. Forexample, a processor (e.g., storage/processing device 120 of FIG. 1) mayadjust the media by stopping, pausing, and/or marking a playbacklocation (e.g. providing a digital bookmark of a specific playbacklocation within an audiobook) of the media.

Step 270 includes not adjusting the media in response to determiningthat the distraction-level value does not exceed the distractionthreshold value.

FIG. 3 illustrates an example plot 300 of generated distraction-levelvalues and distraction threshold values with respect to time. Thedistraction-level value curve 320 may be generated in real time, basedon the personal data of the vehicle operator.

For example, during time T0 to approximately T6, personal data, such asimage data acquired by an IoT camera within the vehicle and heart ratedata from a smartwatch worn by the vehicle operator, may indicate thatthe vehicle operator appears to have an attentive, relaxed mental state;thus, a distraction-level value of approximately 0.40 may be generatedover that time period. During approximately time T6 to T12, the personaldata of the vehicle operator may indicate that the vehicle operator hastransitioned to a slightly agitated mental state; thus, the vehicleoperator's distraction-level value increases to nearly 0.50 during thatperiod. During time T12 to T14, the personal data of the vehicleoperator may indicate that the vehicle operator appears to have a moreattentive, relaxed mental state than during the period T0 to T6; thus, adistraction-level value less than 0.30 may be generated over that timeperiod.

The distraction threshold value curve 310 may be generated in real time,based on the obtained contextual data. For example, during time T0 toapproximately T5, contextual data obtained from a vehicle weather sensorand a vehicle global positioning system may indicate clear weatherconditions and that the vehicle is located in an area commonly traversedby the vehicle operator. Accordingly, a distraction threshold value ofapproximately 0.60 may be generated for that time period. Next, duringapproximately time T5 to T12, the vehicle weather sensor may detect atransition to steady rainfall. Furthermore, the vehicle globalpositioning system may detect that the vehicle is located in an areathat has not been traversed by the vehicle operator. In response to suchcontextual data, the distraction threshold value may decrease toapproximately 0.30 during that time period. During approximately timeT12 to T14, the obtained contextual data may indicate that the vehicleis located in an area that is commonly traversed by the vehicle operatorand that steady rainfall persists. Accordingly, the distractionthreshold value may increase to approximately 0.35 during that timeperiod.

Marker 330 indicates the time when the distraction-level value 320begins to exceed the distraction threshold value 310. At that time, aprocessor (e.g., storage/processing device 120 of FIG. 1) may adjust themedia (e.g., pause, stop, or mark a location of the media for laterreference). Marker 340 indicates the time when the distraction-levelvalue begins to fall below the distraction threshold value. At thattime, a processor (e.g., storage/processing device 120 of FIG. 1) mayprompt the vehicle operator regarding the media. For example, theprocessor may prompt the vehicle operator with options, such asunpausing the media or restarting the playback media from a specificlocation.

FIG. 4 illustrates a flowchart of an example system 400 for determininga distraction-level value using one or more neural networks. One or moreoperations of system 400 may be performed by a processor (e.g.,storage/processing device 120 of FIG. 1).

System 400 includes inputting personal data of the vehicle operator,such as heart rate 410, heart rate variance 420, and images of thevehicle operator 430 into first neural network 440. The first neuralnetwork 440 may then generate a distraction-level value 490. Firstneural network 440 may have been trained to determine one or more mentalstates 450 of the vehicle operator, based on data correlations formedwithin the neural network. For example, the neural network may determinethat the vehicle operator has a frustrated, calm, or anxious mentalstate. Next, the determined mental state of the vehicle operator may beinput into second neural network 480, which may have been trained todetermine relationships between mental states of the vehicle operatorand distraction-level values. Thus, second neural network 480 maydetermine an accurate distraction-level value from a mental state of thevehicle operator.

While, as illustrated, neural networks 440 and 480 are separate neuralnetworks, in some embodiments neural network 440 and neural network 480may functionally be one connected neural network that performs twolinked determinations. In other embodiments, neural networks 440 and 480may be completely separate (e.g., on different processors) such thatoutside intervention may be required to transfer the output from neuralnetwork 440 to neural network 480 (e.g., a first user sending the outputof neural network 440 to a second user, who inputs it into neuralnetwork 480).

While, as illustrated, system 400 includes two neural networks with twoseparate outputs, in some embodiments system 400 may be configureddifferently. For example, system 400 may be configured with a singleneural network that is trained to receive personal data of a vehicleoperator (e.g., heart rate 410, heart variation 420, and images of thevehicle operator 430). That single neural network may be trained toprocess the personal data, and based on recognized patterns within thepersonal data, output a distraction-level value (e.g., distraction-levelvalue 490). In these embodiments, system 400 may not include explicitlyidentifying mental states of the vehicle operator. However, in someinstances, the recognized patterns in the personal data on which theoutput of the distraction-level value is based may also happen tocorrelate with specific mental states of the vehicle operator.

FIG. 5 illustrates a flowchart of an example system 500 for determininga distraction threshold value using one or more neural networks. One ormore operations of system 500 may be performed by a processor (e.g.,storage/processing device 120 of FIG. 1). System 500 includes contextualdata, illustrated here by accelerometer data 510 and IoT sensor data 520being input into neural network 530. While, as illustrated, system 500only discloses utilizing accelerometer data 510 and IoT sensor data 520,in other embodiments any contextual data consistent with the embodimentsof this disclosure may be input into neural network 530 (or an analogousneural network). Neural network 530 then generates a driving behaviorprofile 540, based on data correlations made within the neural network.By generating the driving behavior profile 540, the system 500 may learnand account for the vehicle operator's driving style; thus, the systemmay generate an accurate distraction threshold value for determiningwhen to adjust the media.

For example, contextual data, such as braking patterns from a brakingsensor, vehicle acceleration patterns from an accelerometer, andvelocity measurements from a speedometer may indicate that a vehicleoperator regularly brakes with high frequency, accelerates sharply, andtravels at a high velocity. From this data, neural network 530 maydevelop a driving behavior profile 540 of the vehicle operator. In someembodiments, the driving behavior profile may take the form of aspecific preestablished designation, such as “aggressive driver,” thatcorresponds with the contextual data. In some embodiments, the drivingbehavior profile may take the form of an alphanumeric or graphicaloutput that corresponds with the contextual data. Furthermore, thedriving behavior profile may serve as a baseline for comparinglater-acquired contextual data so that the system 500 may distinguishabnormal driving behavior from customary driving behavior by the vehicleoperator. Accordingly, embodiments of the present disclosure may modifythe distraction threshold value to account for abnormal driving behaviorthat may contribute to the vehicle operator being distracted from themedia.

Processor 570 may receive and analyze the driving behavior profile 540,as well as weather data 550 and location data 560 to determine thedistraction threshold value 580.

FIG. 6 illustrates a block diagram of an example system 600 according tothe present disclosure. System 600 includes a trained machine learningmodule 650, which may include one or more processors and/or neuralnetworks. Learning module 650 may receive personal data, such as heartrate 605, heart rate variance 610, and image data 615 from one or moredevices in communication with the learning module 650. Additionally, animage analysis module 640, which may include one or more processorsand/or neural networks, may analyze the image data 615 before it isreceived by the learning module 650.

For example, image analysis module 640 may implement digital imageanalysis techniques to recognize and characterize facial expressionsappearing in one or more images of a vehicle operator. The imageanalysis module may then transmit the characterizations to the learningmodule in the form of an alphanumeric or graphical output.

Learning module 650 may also receive contextual data, such accelerometerdata 620, vehicle IoT data 625, weather data 630, and location data 635from one or more devices in communication with the learning module 650.Additionally, a driving behavior analysis module 645, which may includeone or more processors and/or neural networks, may analyze theaccelerometer data 620 and the vehicle IoT data 625 before it isreceived by the learning module 650.

The learning module 650 may generate and compare a distraction-levelvalue and a distraction threshold value to determine when to perform amedia adjustment 660.

Neural networks may be trained to recognize patterns in input data by arepeated process of propagating training data through the network,identifying output errors, and altering the network to address theoutput error. Training data that has been reviewed by human annotatorsis typically used to train neural networks. Training data is propagatedthrough the neural network, which recognizes patterns in the trainingdata. Those patterns may be compared to patterns identified in thetraining data by the human annotators in order to assess the accuracy ofthe neural network. Mismatches between the patterns identified by aneural network and the patterns identified by human annotators maytrigger a review of the neural network architecture to determine theparticular neurons in the network that contributed to the mismatch.Those particular neurons may then be updated (e.g., by updating theweights applied to the function at those neurons) in an attempt toreduce the particular neurons' contributions to the mismatch. Thisprocess is repeated until the number of neurons contributing to thepattern mismatch is slowly reduced, and eventually the output of theneural network changes as a result. If that new output matches theexpected output based on the review by the human annotators, the neuralnetwork is said to have been trained on that data.

Once a neural network has been sufficiently trained on training datasets for a particular subject matter, it may be used to detect patternsin analogous sets of live data (i.e., non-training data that have notbeen previously reviewed by human annotators, but that are related tothe same subject matter as the training data). The neural network'spattern recognition capabilities can then be used for a variety ofapplications. For example, a neural network that is trained on aparticular subject matter may be configured to review live data for thatsubject matter and predict the probability that a potential eventassociated with that subject matter is occurring or will occur.

FIG. 7 illustrates an embodiment of an example method 700 for training,using a set of training data, one or more neural networks to predict avehicle operator's distraction from media playback. In some embodiments,training data may be data related to a user's operation of a vehicle ata particular time in the past. In step 710, personal data about avehicle operator at the particular time is input into the neuralnetwork(s). The personal data may include information such as heartrate, heart rate variance, and images of the vehicle operator.Additionally, the data input into the neural network(s) may take variousformats, such as alphanumeric formats or graphical formats.

Step 720 includes obtaining a prediction 720 from the neural networkregarding whether the vehicle operator was distracted from the media atthe particular time. In some embodiments, this prediction may have adirect bearing on whether media adjustment would have been appropriateat the particular time. The prediction may be based on correlationsbetween the input training data that are formed within the neuralnetwork.

Step 730 includes obtaining a vehicle operator's actual action withrespect to the media near the particular time. The vehicle operator'saction with respect to the media near the particular time may be anindication of whether the vehicle operator was distracted from the mediaat the particular time. In some embodiments, for example, the vehicleoperator's actual action may include whether the vehicle operatorstopped playback of the vehicle media at the particular time. In someembodiments, the vehicle operator's actual action may include whetherthe vehicle operator, at some time after the particular time, rewoundthe vehicle media or commanded the vehicle media to skip backwards toreplay the vehicle media at or slightly before or slightly after theparticular point in time.

In some embodiments, the vehicle operator's actual action may becategorized by a human developer of the neural network. For example, ahuman developer may attach an actual distraction value (e.g., “yes” or“no,” “1” or “0,” “TRUE” or “FALSE”) to the actual response that stateswhether the response is indicative of distraction from the media. Inanother example, a list of such actual distraction values may beconnected to various potential vehicle operator actions in a legend/key.In these embodiments, the vehicle operator's actual action could becross referenced against the legend/key to determine whether theappropriate actual distraction value.

Step 740 includes determining whether the obtained prediction matchesthe obtained actual action from the vehicle operator. In someembodiments, this may take the form of comparing the prediction againstan actual distraction value for the actual action. Step 750 includesadjusting the neural network when the obtained prediction does not matchthe obtained response from the vehicle operator (e.g., through backpropagation). Alternatively, step 760 includes not adjusting the neuralnetwork when the obtained prediction does match the obtained responsefrom the vehicle operator.

FIG. 8 depicts the representative major components of an exemplaryComputer System 801 that may be used in accordance with embodiments ofthe present disclosure. The particular components depicted are presentedfor the purpose of example only and are not necessarily the only suchvariations. The Computer System 801 may comprise a Processor 810, Memory820, an Input/Output Interface (also referred to herein as I/O or I/OInterface) 830, and a Main Bus 840. The Main Bus 840 may providecommunication pathways for the other components of the Computer System801. In some embodiments, the Main Bus 840 may connect to othercomponents such as a specialized digital signal processor (notdepicted).

The Processor 810 of the Computer System 801 may be comprised of one ormore CPUs 812. The Processor 810 may additionally be comprised of one ormore memory buffers or caches (not depicted) that provide temporarystorage of instructions and data for the CPU 812. The CPU 812 mayperform instructions on input provided from the caches or from theMemory 820 and output the result to caches or the Memory 820. The CPU812 may be comprised of one or more circuits configured to perform oneor methods consistent with embodiments of the present disclosure. Insome embodiments, the Computer System 801 may contain multipleProcessors 810 typical of a relatively large system. In otherembodiments, however, the Computer System 801 may be a single processorwith a singular CPU 812.

The Memory 820 of the Computer System 801 may be comprised of a MemoryController 822 and one or more memory modules for temporarily orpermanently storing data (not depicted). In some embodiments, the Memory820 may comprise a random-access semiconductor memory, storage device,or storage medium (either volatile or non-volatile) for storing data andprograms. The Memory Controller 822 may communicate with the Processor810, facilitating storage and retrieval of information in the memorymodules. The Memory Controller 822 may communicate with the I/OInterface 830, facilitating storage and retrieval of input or output inthe memory modules. In some embodiments, the memory modules may be dualin-line memory modules.

The I/O Interface 830 may comprise an I/O Bus 850, a Terminal Interface852, a Storage Interface 854, an I/O Device Interface 856, and a NetworkInterface 858. The I/O Interface 830 may connect the Main Bus 840 to theI/O Bus 850. The I/O Interface 830 may direct instructions and data fromthe Processor 810 and Memory 820 to the various interfaces of the I/OBus 850. The I/O Interface 830 may also direct instructions and datafrom the various interfaces of the I/O Bus 850 to the Processor 810 andMemory 820. The various interfaces may comprise the Terminal Interface852, the Storage Interface 854, the I/O Device Interface 856, and theNetwork Interface 858. In some embodiments, the various interfaces maycomprise a subset of the aforementioned interfaces (e.g., an embeddedcomputer system in an industrial application may not include theTerminal Interface 852 and the Storage Interface 854).

Logic modules throughout the Computer System 801—including but notlimited to the Memory 820, the Processor 810, and the I/O Interface830—may communicate failures and changes to one or more components to ahypervisor or operating system (not depicted). The hypervisor or theoperating system may allocate the various resources available in theComputer System 801 and track the location of data in Memory 820 and ofprocesses assigned to various CPUs 812. In embodiments that combine orrearrange elements, aspects of the logic modules' capabilities may becombined or redistributed. These variations would be apparent to oneskilled in the art.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 9, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 9 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 10, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 9) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 10 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and media control logic 96.

As discussed in more detail herein, it is contemplated that some or allof the operations of some of the embodiments of methods described hereinmay be performed in alternative orders or may not be performed at all;furthermore, multiple operations may occur at the same time or as aninternal part of a larger process.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 11 depicts an example neural network 1100 that may be specializedto process a vehicle operator's personal data or mental states. Forexample, neural network 1100 may be specialized to process the personaldata obtained at step 210 in FIG. 2, or mental state 450 of FIG. 4.

Neural network 1100 may be a classifier-type neural network. Neuralnetwork 1100 may be part of a larger neural network. For example, neuralnetwork 1100 may be nested within a single, larger neural network,connected to several other neural networks, or connected to severalother neural networks as part of an overall aggregate neural network.

Inputs 1102-1 through 1102-m represent the inputs to neural network1100. In this embodiment, 1102-1 through 1102-m do not representdifferent inputs. Rather, 1102-1 through 1102-m represent the same inputthat is sent to each first-layer neuron (neurons 1104-1 through 1104-m)in neural network 1100. In some embodiments, the number of inputs 1102-1through 1102-m (i.e., the number represented by m) may equal (and thusbe determined by) the number of first-layer neurons in the network. Inother embodiments, neural network 1100 may incorporate 1 or more biasneurons in the first layer, in which case the number of inputs 1102-1through 1102-m may equal the number of first-layer neurons in thenetwork minus the number of first-layer bias neurons. In someembodiments, a single input (e.g., input 1102-1) may be input into theneural network. In such an embodiment, the first layer of the neuralnetwork may comprise a single neuron, which may propagate the input tothe second layer of neurons.

Inputs 1102-1 through 1102-m may comprise one or more values of personaldata or one or more mental states. Neural network 1100 comprises 5layers of neurons (referred to as layers 1104, 1106, 1108, 1110, and1112, respectively corresponding to illustrated nodes 1104-1 to 1104-m,nodes 1106-1 to 1106-n, nodes 1108-1 to 1108-o, nodes 1110-1 to 1110-p,and node 1112). In some embodiments, neural network 1100 may have morethan 5 layers or fewer than 5 layers. These 5 layers may each comprisethe same amount of neurons as any other layer, more neurons than anyother layer, fewer neurons than any other layer, or more neurons thansome layers and fewer neurons than other layers. In this embodiment,layer 1112 is treated as the output layer. Layer 1112 may output aprobability (e.g., that a vehicle operator is distracted), and containsonly one neuron (neuron 1112). In other embodiments, layer 1112 maycontain more than 1 neuron. For example, layer 1112 may output theprobability that a vehicle operator is in several mental states, and maycontain one neuron for each of those mental states. In this illustrationno bias neurons are shown in neural network 1100. However, in someembodiments each layer in neural network 1100 may contain one or morebias neurons.

Layers 1104-1112 may each comprise an activation function. Theactivation function utilized may be, for example, a rectified linearunit (ReLU) function, a SoftPlus function, a Soft step function, orothers. Each layer may use the same activation function, but may alsotransform the input or output of the layer independently of or dependentupon the ReLU function. This is also true in embodiments with morelayers than are illustrated here, or fewer layers.

Layer 1112 is the output layer. In this embodiment, neuron 1112 producesoutputs 1114 and 1116. Outputs 1114 and 1116 represent complementaryprobabilities that a target event will or will not occur. For example,output 1114 may represent the probability that a vehicle operator isdistracted, and output 1116 may represent the probability that vehicleoperator is not distracted. In some embodiments, outputs 1114 and 1116may each be between 0.0 and 1.0, and may add up to 1.0. In suchembodiments, a probability of 1.0 may represent a projected absolutecertainty (e.g., if output 1114 were 1.0, the projected chance that thetarget event would occur would be 100%, whereas if output 1116 were 1.0,the projected chance that the target event would not occur would be100%).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the variousembodiments. As used herein, the singular forms “a,” “an,” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“includes” and/or “including,” when used in this specification, specifythe presence of the stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. In the previous detaileddescription of example embodiments of the various embodiments, referencewas made to the accompanying drawings (where like numbers represent likeelements), which form a part hereof, and in which is shown by way ofillustration specific example embodiments in which the variousembodiments may be practiced. These embodiments were described insufficient detail to enable those skilled in the art to practice theembodiments, but other embodiments may be used, and logical, mechanical,electrical, and other changes may be made without departing from thescope of the various embodiments. In the previous description, numerousspecific details were set forth to provide a thorough understanding thevarious embodiments. But, the various embodiments may be practicedwithout these specific details. In other instances, well-known circuits,structures, and techniques have not been shown in detail in order not toobscure embodiments.

Different instances of the word “embodiment” as used within thisspecification do not necessarily refer to the same embodiment, but theymay. Any data and data structures illustrated or described herein areexamples only, and in other embodiments, different amounts of data,types of data, fields, numbers and types of fields, field names, numbersand types of rows, records, entries, or organizations of data may beused. In addition, any data may be combined with logic, so that aseparate data structure may not be necessary. The previous detaileddescription is, therefore, not to be taken in a limiting sense.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A method comprising: obtaining personal data of a vehicle operator,wherein the vehicle operator is operating a vehicle while media playbackoccurs within the vehicle, obtaining contextual data about theoperating, generating a distraction-level value based at least in parton the personal data, wherein the distraction-level value represents apotential degree to which the vehicle operator is distracted from themedia, generating a distraction threshold value based at least in parton the contextual data, comparing the distraction-level value to thedistraction threshold value, determining at a first time that thedistraction-level value exceeds the distraction threshold value, andadjusting the media in response to the determining at the first time. 2.The method of claim 1, wherein generating a distraction-level valuecomprises determining a mental state of the vehicle operator and whereinthe distraction-level value is based at least in part on the determinedmental state of the vehicle operator.
 3. The method of claim 1, whereinadjusting the media comprises stopping the media playback.
 4. The methodof claim 3, further comprising: determining at a second time that thedistraction threshold value exceeds the distraction-level value, whereinthe second time is subsequent to the first time, and resuming mediaplayback in response to the determining at the second time.
 5. Themethod of claim 1, wherein the personal data of the vehicle operatorcomprises a heart rate variance of the vehicle operator.
 6. The methodof claim 1, wherein generating a distraction threshold value comprisesdeveloping a driving behavior profile of the vehicle operator.
 7. Asystem comprising: a memory; and a processor communicatively coupled tothe memory, wherein the processor is configured to perform a methodcomprising: obtaining personal data of a vehicle operator, wherein thevehicle operator is operating a vehicle while media playback occurswithin the vehicle, obtaining contextual data about the operating,generating a distraction-level value based at least in part on thepersonal data, wherein the distraction-level value represents apotential degree to which the vehicle operator is distracted from themedia, generating a distraction threshold value based at least in parton the contextual data, comparing the distraction-level value to thedistraction threshold value, determining at a first time that thedistraction-level value exceeds the distraction threshold value, andadjusting the media in response to the determining at the first time. 8.The system of claim 7, wherein generating a distraction-level valuecomprises determining a mental state of the vehicle operator and whereinthe distraction-level value is based at least in part on the determinedmental state of the vehicle operator.
 9. The system of claim 7, whereinadjusting the media comprises stopping the media playback.
 10. Thesystem of claim 9, further comprising: determining at a second time thatthe distraction threshold value exceeds the distraction-level value,wherein the second time is subsequent to the first time, and resumingmedia playback in response to the determining at the second time. 11.The system of claim 7, wherein the personal data of the vehicle operatorcomprises a heart rate variance of the vehicle operator.
 12. The systemof claim 7, wherein generating a distraction threshold value comprisesdeveloping a driving behavior profile of the vehicle operator.
 13. Acomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to perform a methodcomprising: obtaining personal data of a vehicle operator, wherein thevehicle operator is operating a vehicle while media playback occurswithin the vehicle, obtaining contextual data about the operating,generating a distraction-level value based at least in part on thepersonal data, wherein the distraction-level value represents apotential degree to which the vehicle operator is distracted from themedia, generating a distraction threshold value based at least in parton the contextual data, comparing the distraction-level value to thedistraction threshold value, determining at a first time that thedistraction-level value exceeds the distraction threshold value, andadjusting the media in response to the determining at the first time.14. The computer program product of claim 13, wherein generating adistraction-level value comprises determining a mental state of thevehicle operator and wherein the distraction-level value is based atleast in part on the determined mental state of the vehicle operator.15. The computer program product of claim 13, wherein adjusting themedia comprises stopping the media playback.
 16. The computer programproduct of claim 15, further comprising: determining at a second timethat the distraction threshold value exceeds the distraction-levelvalue, wherein the second time is subsequent to the first time, andresuming media playback in response to the determining at the secondtime.
 17. The computer program product of claim 13, wherein the personaldata of the vehicle operator comprises a heart rate variance of thevehicle operator.
 18. The computer program product of claim 13, whereingenerating a distraction threshold value comprises developing a drivingbehavior profile of the vehicle operator.